Melania Nowicka

This study evaluates data leakage effects in protein language model pretraining on thermostability prediction by comparing dataset split strategies.

This paper evaluates prompt tuning as a cost-efficient alternative to fine-tuning protein language models for sequence generation, finding it more efficient in resource usage but less effective than fine-tuning in sequence quality.

The paper curates a fungal host range dataset and uses deep learning to detect novel fungal pathogens from DNA sequences.

CovRadar provides a platform for tracking and filtering SARS-CoV-2 mutations in the spike gene, enabling users to analyze and visualize mutational changes and spatio-temporal distributions.

This paper presents a framework for designing and evaluating miRNA-based distributed cell classifiers, showing improved prediction accuracy over single-circuit designs.

This paper designs distributed cell classifiers using a genetic algorithm to improve accuracy and flexibility in cancer-type specific cell targeting.